Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Quantitative risk assessment for developmental toxicity.

L Ryan1

  • 1Harvard School of Public Health, Boston, Massachusetts.

Biometrics
|March 1, 1992
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

SCANBIT facilitates identification of tumor cell populations in scRNAseq data using pseudobulked SNV calls.

bioRxiv : the preprint server for biology·2026
Same author

Response-guided neoadjuvant sacituzumab govitecan for localized triple-negative breast cancer: results from the NeoSTAR trial.

Annals of oncology : official journal of the European Society for Medical Oncology·2023
Same author

Luck of the Microbiologist - A Retrospective Observational Cohort Study.

Irish medical journal·2023
Same author

Cementless versus cemented unicompartmental knee arthroplasty: a systematic review of comparative studies.

Musculoskeletal surgery·2023
Same author

Investigating Effects of Mentoring for Youth with Assault Injuries: Results of a Randomized-Controlled Trial.

Prevention science : the official journal of the Society for Prevention Research·2022
Same author

Neonates and COVID-19: state of the art : Neonatal Sepsis series.

Pediatric research·2021
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces a new statistical method for developmental toxicity testing, improving risk assessment for multiple adverse outcomes. The proposed approach provides a more accurate estimation of acceptable exposure levels, enhancing regulatory decision-making.

Area of Science:

  • Toxicology
  • Biostatistics
  • Risk Assessment

Background:

  • Current statistical methods for developmental toxicity experiments primarily focus on binary outcomes like malformations.
  • The litter effect, a key factor in developmental toxicity, is often inadequately addressed by existing binary models.
  • Quantitative risk assessment for a spectrum of adverse effects remains underdeveloped.

Purpose of the Study:

  • To develop and propose a statistical method for quantitative risk assessment in developmental toxicity studies.
  • To address the challenge of assessing overall risk when exposures cause multiple adverse outcomes (resorption, fetal death, malformations).
  • To establish an exposure level where the overall risk of any adverse effect is acceptably low.

Main Methods:

Related Experiment Videos

  • Utilizes a continuation ratio formulation of a multinomial distribution.
  • Incorporates an additional scale parameter to account for overdispersion in the data.
  • Compares the proposed method with existing binary models for prenatal death, malformation, and a combined normal/abnormal classification.

    • Main Results:

    • Risk assessments based on single outcomes (e.g., malformations) can be overly conservative.
    • The proposed method provides a more comprehensive risk assessment by considering multiple adverse effects.
    • Demonstrates the application and effectiveness of the method using data from developmental toxicity studies.

    Conclusions:

    • The proposed statistical method offers a more appropriate approach to risk assessment in developmental toxicity.
    • Considering all adverse effects leads to more accurate and less conservative exposure level estimations.
    • This methodology enhances regulatory decision-making by providing a robust framework for evaluating developmental toxicity risks.